UKF

UKF is applied to nonlinear initial alignment of SINS on the stationary base .

将UKF方法应用于静基座对准，研究了非线性静基座对准问题。

This paper focuses on the autonomous relative guidance algorithm based on the UKF in the process of proximity .

本文研究了基于UKF滤波器的自主相对导航算法。

The sensor nodes based on their orientation process with the Unscented Kalman Filter ( UKF ) target tracking method .

把传感器节点的自身定位过程用基于无迹卡尔曼滤波（UKF）的目标跟踪方法实现。

Aiming at this problem , an improved UKF algorithm based on spherical sampling and square-root filtering is presented .

改进的UKF滤波应用了超球面采样和平方根滤波方法，降低了算法的计算量，提高了滤波过程中的数值稳定性。

The adaptive UKF algorithm was adopted to solve the non-linear and filter volatilization problems . A sample system was made out to be tested .

文中针对组合滤波中的非线性和滤波发散等问题，采用自适应UKF算法解决，完成了系统原理样机的设计实现，并进行了相关测试。

The experimental results show that the UKF algorithm outperforms the other two in accuracy while its time cost is very much close to the KF algorithm .

实验结果证明，使用UKF算法的跟踪精度优于其他两种算法，时间耗费仅次于KF算法。

In order to achieve higher accuracy in nonlinear / non-Gaussian state estimation , this paper proposes a new unscented Kalman filter ( UKF ) .

为了在非线性、非高斯系统估计中获得更好的精度，提出一种新的unscented卡尔曼滤波（UKF）。

An initial alignment method for low-cost SINS is proposed , which uses nonlinear error model and the unscented Kalman filter ( UKF ) .

提出适用于低成本捷联惯导系统的初始对准方法，即采用非线性对准模型和采样卡尔曼滤波（UnscentedKalmanFilter）进行状态估计。

After analyzing the model of high dynamic signals , a quasi-open-loop carrier tracking method based on unscented Kalman filter ( UKF ) is proposed .

在分析高动态载波信号模型的基础上，提出了一种基于无迹卡尔曼滤波（UKF）的准开环载波跟踪方法。

Successful using UKF algorithm , which deals with the strong nonlinear problem of orbital determination in the starlight angle method , makes system to belong to better performance .

星光角距定轨法由于使用了UKF滤波方法解决了定轨中的严重非线性问题，在不增加计算量的前提下提高了定轨的精度。

To solve filtering divergence , the Unscented Kalman filter ( UKF ) algorithm , which has better non-linear approximation ability , is adopted ;

为了改善纯角度跟踪的滤波发散问题，一方面，在滤波处理时采用了非线性逼近能力更强的Unscented卡尔曼滤波算法；

The algorithm adopts a new proposal distribution combining the unscented Kalman filter ( UKF ) with the adaptive strong tracking filter ( STF ) .

该算法采用一种新提议分布，即将UKF（UnscentedKalmanFilter）与自适应强跟踪滤波器（STF）相结合。

Concerning the problem of three-axis stabilized satellite attitude estimation from vector observations , a modified unscented Kalman filter ( UKF ) algorithm is derived .

针对矢量观测的三轴稳定卫星的姿态估计问题，提出了一种改进的UKF（UnscentedKalmanFilter）滤波算法。

Unscented Kalman Filter ( UKF ) is a new filtering method , which is superior to the EKF in accomplishment and in estimation precision .

Unscented卡尔曼滤波（UKF）在算法实现和估计精度方面均优于传统的扩展卡尔曼滤波（EKF）。

Consequently , the UKF is suitable for the real-time attitude estimation of the two-wheeled self-balanced robot in the fast and maneuverable process .

因此，UKF能够满足两轮自平衡机器人快速机动过程中的实时姿态估计要求。

The unscented Kalman filter ( UKF ) is applied to the radar registration and a new algorithm for the multi-radar azimuth registration is presented .

将无味卡尔曼滤波（UnscentedKalmanFilter，UKF）应用于雷达配准，提出一种新的多雷达方位配准算法。

Therefore , a nonlinear adaptive Kalman filter based on fictitious noise compensating technique and an unscented Kalman filter ( UKF ) are used to estimate vehicle states .

为此，利用基于虚拟噪声补偿技术的非线性自适应滤波算法和无迹卡尔曼滤波算法对汽车的行驶状态进行估计。

This paper presents a real-time background subtraction and moving shadow detection method based on Unscented Kalman Filter ( UKF ), and constructs the whole frame for moving object detection .

提出了基于无偏卡尔曼滤波器（UKF）的背景提取和阴影检测方法，构建整体的运动物体检测框架。

For satellite formation flying orbit adjustment situation , which only used radio measurement , a strong tracking unscented Kalman filter ( UKF ) algorithm was introduced to the simulation .

并针对编队卫星进行轨道机动时仅采用无线电进行测量的工况，采用强跟踪离散卡尔曼滤波（UKF）算法进行仿真计算。

For passive bistatic radar target tracking problem , the performances of several nonlinear filtering algorithms such as EKF , UKF and CDF were simulated and analyzed .

针对无源双基地雷达目标跟踪问题，仿真分析了EKF、UKF、CDF等几种非线性滤波算法的状态估计性能。

A new improved particle filter algorithm with the simplified UT ( unscented transformation ) and the modified unscented Kalman filter ( UKF ) proposal distribution is presented .

简化UT（unscentedtransformation）转化参数，修改UKF（UnscentedKalmanFilter）提议分布，提出了改进的粒子滤波算法。

With the map information , robot uses extended kalman filter ( UKF ) algorithm to locate . ( 3 ) Introduce a algorithm of multi-sensor data fusion and its structure .

将获得的地图信息，利用扩展卡尔曼滤波算法实现机器人定位，并进行仿真实验。（3）研究了一种基于多传感器信息融合的路径规划算法。

Unscented Kalman Filter ( UKF ), which is an evolutional algorithm of Extended Kalman Filter ( EKF ), has been successfully applied in many nonlinear estimation problems .

无轨迹卡尔曼滤波器（UKF）作为扩展卡尔曼滤波器（EKF）的进化算法在许多非线性估计问题上取得了成功的应用。

According to the similar computation process of UKF and extended Kalman filter ( EKF ), the combined Kalman filter based on SSUKF and EKF was designed .

根据UKF和扩展卡尔曼滤波（EKF）计算过程相似的特点，设计了SSUKF和EKF相结合的混合卡尔曼滤波算法。

In order to yield the higher accuracy of navigation , a novel method & Unscented Kalman Filter ( UKF ) was employed in state estimation for a land vehicle navigation system .

为了获得更高的导航精度，将一种新的滤波方法&UKF方法用于车载导航系统的状态估计中。

An autonomous attitude determination algorithm based on Unscented Kalman Filter ( UKF ) for low-Earth orbit ( LEO ) satellite is developed by processing geomagnetic field measurements .

提出了利用UKF（UnscentedKalmanFilter）处理地磁场测量数据进行低轨道（LEO）卫星自主定姿的算法。

In view of nonlinear feature of measure equation , a ground target tracking algorithm based on the unscented kalman filter ( UKF ) is given . Finally , the simulation experiment is analyzed .

针对雷达测量模型非线性的特点，提出了基于无迹卡尔曼滤波UKF算法的地面目标跟踪算法，并进行了仿真试验分析。

The Unsctened Kalman Filter ( UKF ) for nonlinear system is applied in passive locating by single station , and the general UKF is improved according to the specific application .

将一种适用于非线性系统的UKF应用于单站无源定位，并结合具体应用背景，对通常的UKF作了适当的改进。

The results of Monte Carlo simulations have demonstrated that the UKF performs nearly the same as the EKF does in accuracy for the above special background with weak nonlinearity .

表明UKF在该特定应用背景下，由于模型的非线性较弱，使得UKF在精度上与EKF相当，而且在运算量上也有所增加。

The new filter utilizes the MEP process of Nonlinear Predictive Filter ( NPF ), which can adjust the inaccurate model in real time and thus remedy the shortage of the UKF .

它利用非线性预测滤波器（NPF）的模型误差预测过程，能够对不准确的系统模型进行实时修正，弥补了UKF方法的不足。